Giving Credit where Credit Is Due

ACM recognizes excellence through its eminent awards for technical and professional achievements and contributions in computer science and information technology. It also names as Fellows and Distinguished Members those members who, in addition to professional accomplishments, have made significant contributions to ACM's mission. ACM awards recognize achievements by young computing professionals, educators, theoretical computer scientists, software systems innovators, and pioneers who have made humanitarian and cross-discipline contributions.

How to Nominate

Award nominations deadlines occur throughout the year, with a heavy concentration in the Fall. Please refer to the Nomination Process page for each award, which includes not only information about the deadline but also guidance for preparing each type of nomination. ACM's conflict-of-interest guidelines apply to all award nominations.

Daphne Koller

USA - 2007

citation

For her work on combining relational logic and probability that allows probabilistic reasoning to be applied to a wide range of applications, including robotics, economics, and biology.

Prof. Koller's work on combining relational logic and probability is the most important of her many research contributions in Artificial Intelligence and Computer Science. It has transformed the way people handle uncertainty in large computer systems, such as heterogeneous databases, image understanding systems, biological and medical models, and natural language processing systems.

Her interest in this topic stems from her 1994 PhD dissertation. At that time logical reasoning and probabilistic reasoning were two distinct sub-fields in AI, with very little interaction. Prof. Koller and a few others recognized that relational logic and probability were complementary. Relational logic brings expressive power, but cannot handle the uncertainty that is inherent in most real-world domains. On the other hand, probability (and probability-based knowledge representation tools, like Bayesian networks and Markov models) provides a sound methodology for dealing with uncertainty, but is unable to reason about objects and the relations between them. The combination of relational logic and probability led to a new knowledge representation paradigm, known as relational probabilistic models. Prof. Koller's Computers and Thought Award Lecture at IJCAI 2001 established it as a major research area in AI.

Aside from establishing the foundations, Prof. Koller's algorithmic work brought these models into the realm of feasibility. A good knowledge representation language must be expressive, and must also support efficient inference and learning algorithms. During the last decade, she and her students have both added to the theory and built operational systems that apply these ideas to real-world domains involving millions of objects, variables, and relations. Her learning algorithms make it possible to construct large models of complex domains, for example in biology, epidemiology, and computer vision. Her inference algorithms make it possible to evaluate the probability of a query in a way that exploits all of the information available about the huge number of inter-linked objects in the domain.

Prof. Koller's work has influenced many other areas of computer science and other fields, including information retrieval from heterogeneous databases, natural language understanding, robotics, machine perception, economics, and biology. She has applied her approaches to a number of important real problems, including biological problems and robot perception problems.